Fighting to Make GTK+, Glade, and Python Play in Windows

These screenshots show me running the Py2EXE-compiled script I wrote last weekend on a Windows 7 machine. Additionally there is a screenshot of the “Add/Remove Programs” window demonstrating which versions of which libraries were required.glade_exeneedToInstall


Python Script with GTK+ GUI Compiled with Py2EXE

Wow, that’s a mouthfull. This is a total hack, but it works — and barely I might add! I spent all night jumping through hoops to get this thing to run on Windows. The problem is that I designed my previous UI in a version of GLADE which is newer than that supported by Windows. It looks like it’s not backward-compatible, so I have to re-design the GUI from scratch using an earlier version of GLADE. I’ll probably stick to GTK version 2.12 and Python version 2.6 because they play nicely on Windows. It’s a quick and dirty script, but I was able to make the following run on Windows as a single EXE file!glade_windows_python



Spectrograph UI Made with Glade

While continuing to investigate my options for the new version of QRSS VD, I re-visited Glade, the GTK GUI designer. In short, it lets you draw widgets (combo boxes, scrollbars, labels, images, buttons, etc) onto windows and then makes it easy to add code to the GUI. I *hated* the old QRSS VD development because of the ridiculously large amount of time I had to spend coding the UI. Hopefully by migrating from TKinter to GTK – while it opens a whole new can of worms – will let me add functionality rapidly without hesitation.glade_python_improving

Here’s a quick screenshot of my running this new version of the software with a GUI I made in less than an hour. The bars for brightness and contrast can be adjusted which modify the spectrograph in real time. The audio is whatever is playing in Pandora. I like the “fantastic plastic machine” radio station!


Fast TK Pixelmap generation from 2D Numpy Arrays in Python

I had TKinter all wrong! While my initial tests with PyGame’s rapid ability to render Numpy arrays in the form of pixel maps proved impressive, it was only because I was comparing it to poor TK code. I don’t know what I was doing wrong, but when I decided to give TKinter one more shot I was blown away — it’s as smooth or smoother as PyGame. Forget PyGame! I’m rendering everything in raw TK from now on. This utilizes the Python Imaging Library (PIL) so it’s EXTREMELY flexible (supports fancy operations, alpha channels, etc).fast_scroll_tk

The screenshot shows me running the script (below) generating random noise and “scrolling” it horizontally (like my spectrograph software does) at a fast rate smoothly (almost 90 FPS!). Basically, it launches a window, creates a canvas widget (which I’m told is faster to update than a label and reduces flickering that’s often associated with rapid redraws because it uses double-buffering). Also, it uses threading to handle the calculations/redraws without lagging the GUI. The code speaks for itself.

import Tkinter
from PIL import Image, ImageTk
import numpy
import time

class mainWindow():

	def __init__(self):
		self.root = Tkinter.Tk()
		self.frame = Tkinter.Frame(self.root, width=500, height=400)
		self.canvas = Tkinter.Canvas(self.frame, width=500,height=400),y=-2)
		self.root.after(0,self.start) # INCREASE THE 0 TO SLOW IT DOWN

	def start(self):
		global data'L', ([1],[0]),'b').tostring()) = ImageTk.PhotoImage(
		if self.times%33==0:
			print "%.02f FPS"%(self.times/(time.clock()-self.timestart))

if __name__ == '__main__':


Detrending Data in Python with Numpy

While continuing my quest into the world of linear data analysis and signal processing, I came to a point where I wanted to emphasize variations in FFT traces. While I am keeping my original data for scientific reference, visually I want to represent it emphasizing variations rather than concentrating on trends. I wrote a detrending function which I’m sure will be useful for many applications:detrend_fft

def detrend(data,degree=10):
	for i in range(degree,len(data)-degree):
	return detrended+[None]*degree

However, this method is extremely slow. I need to think of a way to accomplish this same thing much faster. [ponders]

UPDATE: It looks like I’ve once again re-invented the wheel. All of this has been done already, and FAR more efficiently I might add. Simply:

import scipy.signal

Now I’m looking into scipy.signal.triang()